Non-local tensor completion for multitemporal remotely sensed images inpainting

TL;DR

This paper introduces a non-local low-rank tensor completion method for reconstructing missing areas in multitemporal remotely sensed images, leveraging spatial, spectral, and temporal correlations for improved accuracy and efficiency.

Contribution

The paper proposes a novel non-local tensor completion approach that effectively utilizes multi-dimensional correlations for image inpainting, outperforming existing patch-based methods.

Findings

Effective reconstruction of missing image data demonstrated on real and simulated datasets.

The proposed method outperforms recent patch-based methods in both qualitative and quantitative metrics.

Computational efficiency is improved compared to existing techniques like PM-MTGSR.

Abstract

Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method.